# Load packages 
library(pacman)
pacman::p_load(glue)
pacman::p_load(DT)
pacman::p_load(magick)

This document shows the results of the meta-analysis of the studies: E_MEXP_1425, GSE2508, GSE29718, GSE64567, GSE92405, GSE141432, GSE205668 para el contraste Ob.F - C.F.
 

Taking a p-value adjusted for BH < 0.05 we found 310 genes significativos.
 

Table of results

 

DT = get(load("C:/Users/roxya/OneDrive/Documentos/01Master_bioinformatica/00TFM/05Paper/AnalysisGit/Data/MA/Obesity/IOF/Meta-analysis_IOF_DF.RData"))
datatable(DT, options = list(caption = "Meta-analysis results.",
                              scrollX = TRUE),
                              filter = "top")

 

Graphical representation

 

Here are the characteristic figures of the meta-analysis for the significant genes taking a p-value adjusted for BH < 0.05.
 

# Plot function
grid_img <- function(list_fig, dir, patt, dim = 300){
  # Abrimos todas las filas:
  forest_plots = c()
  funnel_plots = c()
  influ_plots = c()
  for (fig in list_fig){
    f1 = glue("{fig}forest.svg")
    out1 <- magick::image_read(paste0(dir, "/", f1))
    forest_plots = c(forest_plots, image_scale(out1, dim))
    
    f2 = glue("{fig}funnel.svg")
    out2 <- magick::image_read(paste0(dir,"/", f2))
    funnel_plots = c(funnel_plots, image_scale(out2, dim))  
    
    f3 = glue("{fig}influence.svg")
    out3 <- magick::image_read(paste0(dir,"/", f3))
    influ_plots = c(influ_plots, image_scale(out3, dim))
  } 
  
  lim = length(forest_plots)
  grid = NULL
  for ( i in seq(list_fig)){
    a = forest_plots[[i]]
    b = funnel_plots[[i]]
    c = influ_plots[[i]]
    
    fila <- image_append(c(a, b, c))
    
    if(is.null(grid)){
      grid = fila
    }else{
      grid = image_append(c(grid,fila),  stack = TRUE)
    } 
  }
    return(grid)
}